Request-based knowledge acquisition

ABSTRACT

One implementation provides a method for acquiring knowledge from multiple knowledge bases in a knowledge repository. The method includes identifying first and second knowledge bases within the knowledge repository by analyzing a search request received from a client system. The first knowledge base contains knowledge of a first type and the second knowledge base contains knowledge of a second type. The method further includes generating instructions that, when executed, cause first and second requests to be sent to the knowledge repository in sequential fashion to acquire knowledge from the first and second knowledge bases, such that the second request is sent after the first request, and such that the second request includes knowledge of the first type from the first knowledge base acquired in response to the first request.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 10/873,604, filed on Jun. 22, 2004 now U.S. Pat. No. 7,720,862,entitled “REQUEST-BASED KNOWLEDGE ACQUISITION” the contents of which arehereby incorporated by reference.

TECHNICAL FIELD

This application relates to request-based knowledge acquisition incomputing systems.

BACKGROUND

In today's technology age, information and information sources areplentiful. On the World Wide Web, for example, individuals are capableof obtaining information from all over the world. Database and webservers may provide users with information about fixing a car, buyingproducts or services, and the like. By using search engines, anindividual can quickly and easily search for information by entering aseries of search terms.

Search engines often provide compilation and retrieval services. Oneexample of a compilation service involves the use of “spiders” thatcrawl through the World Wide Web and search for web sites and web-sitecontent. The information from these web sites is then compiled intosearch indexes. A master index may be used to store references to thevarious web sites and also to store information contained in theweb-site content. Certain terms may be associated with the entriesstored in the master index. Then, when an individual user enters one ormore search terms during a search operation, the search enginereferences its master index to locate web-site references or web-sitecontent associated with terms that match those from the user's searchrequest.

Because of the growing amount of data contained within the World WideWeb and other information sources, it often may be difficult for usersto obtain all of the information they need by using only a single searchrequest. For example, if a user wants to obtain information from withina company's Intranet, the user may need to execute multiple searches andaccess multiple knowledge bases, or information sources, to retrieve allof the needed information. Alternatively, a service field agent may needto execute multiple searches and collect information from variousdifferent sources when interacting with customers.

SUMMARY

Various implementations are provided herein. One implementation providesa method for acquiring knowledge from multiple knowledge bases in aknowledge repository. The method includes identifying first and secondknowledge bases within the knowledge repository by analyzing a searchrequest received from a client system. The first knowledge base containsknowledge of a first type and the second knowledge base containsknowledge of a second type. The method further includes generatinginstructions that, when executed, cause first and second requests to besent to the knowledge repository in sequential fashion to acquireknowledge from the first and second knowledge bases, such that thesecond request is sent after the first request, and such that the secondrequest includes knowledge of the first type from the first knowledgebase acquired in response to the first request.

Various implementations may provide certain benefits and advantages. Forexample, a user of a mobile device can submit a request to acquireneeded information via a free-text description or selections from a setof predefined fields with values. The request can be parsed and analyzedusing an existing natural language processing tool to create a set ofpossible search/retrieval requests against possible knowledge bases.Based on the set of identified requests, a script generator can furtherdetermine a proper sequence and additional retrieval rules to improvecontent relevancy and retrieval performance. Once the script isgenerated, the script will be automatically executed to acquireknowledge from selected knowledge bases.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a system that may be used to acquireknowledge from a knowledge repository, according to one implementation.

FIG. 2 is a use-case diagram showing interactions between variouscomponents in FIG. 1, according to one implementation.

FIG. 3 is a flow diagram of a method to acquire knowledge from theknowledge repository shown in FIG. 1, according to one implementation.

FIG. 4 is a block diagram of a computing device that may be part of theclient system or the knowledge acquisition engine shown in FIG. 1,according to one implementation.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a system 100 that may be used to acquireknowledge from a knowledge repository 120, according to oneimplementation. In this implementation, the system 100 includes a clientsystem 102 that is coupled to a knowledge acquisition engine 106. Theknowledge repository 120 is contained within the knowledge acquisitionengine 106. During operation, the client system 102 sends a searchrequest to the knowledge acquisition engine 106. The knowledgeacquisition engine 106 analyzes the search request to identify two ofthe knowledge bases 122A and 122B, for example, within the knowledgerepository 120. The knowledge repository 120 also contains knowledgebases 122C and 122D. The knowledge acquisition engine 106 thenautomatically generates a script having instructions that, whenexecuted, cause the knowledge acquisition engine 106 to send tworequests to the knowledge repository 120 in sequential fashion toacquire knowledge from the two identified knowledge bases 122A and 122B.The first request is sent to the knowledge base 122A. The second requestis sent to the knowledge base 122B and includes knowledge from theknowledge base 122A acquired in response to the first request. Theknowledge that is acquired from the knowledge bases 122A and 122B isthen routed back to the client system 102. In this fashion, the clientsystem 102 is able to provide a single request to the knowledgeacquisition engine 106 and thereby obtain knowledge from variousdifferent knowledge bases within the knowledge repository 120. Theknowledge acquisition engine 106 manages the detailed flow ofcommunication with these knowledge bases.

The client system 102 includes an interaction component 104. Theinteraction component 104 manages the flow of information to and fromthe knowledge acquisition engine 106. In one implementation, the clientsystem 102 is a mobile device, such as a personal digital assistant(PDA), that uses a wireless interface to communicate with the knowledgeacquisition engine 106. The knowledge acquisition engine 106 alsoincludes a request parser 108, a query script generator 110, anavigation index generator 112, a visualization output generator 114, anatural language parser 116, and a work package engine 118.

The request parser 108 is coupled to the interaction component 104 ofthe client system 102. The request parser 108 processes requests thatare sent to the knowledge acquisition engine 106 from the client system102. For example, the request parser 108 may analyze an incoming requestand interpret various fields and/or formats of the request. The requestparser 108 is coupled to the natural language parser 116 and may usesthe natural language parser 116 when processing incoming requests sentfrom the client system 102. The natural language parser 116 is able tohelp process requests sent from the client system 102 having natural orfree-text components. For example, a user of the client system 102 mayenter various search terms or keywords in English using a free-textform. These terms or keywords are then provided in a request that issent by the client system 102 to the knowledge acquisition engine 106.The natural language parser 116 and the request parser 108 are able toprocess this request. In another scenario, the request sent by theclient system 102 may be a formatted request having a predefinedstructure. In this scenario, the request parser 108 may be able todirectly process the incoming request without using the natural languageparser 116.

The request parser 108 is also coupled to the query script generator110. The query script generator 110 automatically generates the scriptsthat are used to query the knowledge bases 122A, 122B, 122C, and/or 122Dto acquire knowledge, or information, that can be sent back to theclient system 102. For example, in one scenario, the knowledge base 122Amay contain customer information and the knowledge base 122B may containproduct information. The generated script, when executed, is able tocause knowledge to be acquired for both customer and productinformation. In one implementation, the scripts generated by the queryscript generator 110 include computer-executable instructions forquerying the knowledge bases 122A and 122B in a sequential fashion.These instructions analyze information acquired from a first knowledgebase and use this information as input when processing the request thatis sent to the second knowledge base.

The query script generator 110 is coupled to the work package engine118, which is coupled to the knowledge bases 122A, 122B, 122C, and 122Din the knowledge repository 120. The work package engine 118 uses thescript generated by the script generator 110 to send requests to andacquire knowledge from the knowledge bases 122A, 122B, 122C, and/or122D. The work package engine 118 is further coupled to the navigationindex generator 112 and the visualization output generator 114. Thenavigation index generator 112 generates a navigation index for theknowledge that has been acquired from the knowledge bases 122A, 122B,122C, and/or 122D. The client system 102 is able to use the navigationindex that is generated by the navigation index generator 112 tonavigate through and identify knowledge that has been provided by theknowledge acquisition engine 106. The visualization output generator 114generates a visualization scheme that can be used by the client system102 when navigating through the acquired knowledge. For example, thevisualization output generator 114 may generate a specific form ofgraphical user interface (GUI) that is well suited for display purposeson the client system 102. The navigation index generator 112 andvisualization output generator 114 are each coupled to the interactioncomponent 104 in the client system 102.

The knowledge bases 122A, 122B, 122C, and 122D each contain knowledge ofa particular type. For example, the knowledge base 122A, 122B, 122C, or122D could contain task guidance knowledge to provide guidance whenperforming certain types of tasks, service history knowledge for serviceorders, solution knowledge to help solve problems, sales historyknowledge, spare parts knowledge, service contract knowledge, pricinginformation knowledge, product information knowledge, or sales contractknowledge. In one implementation, each of the knowledge bases 122A,122B, 122C, and 122D contain different types of knowledge.

In one implementation, the request parser 108 is capable of determiningif individual knowledge bases can be accessed independently to satisfy asearch request that is sent by the client system 102. For example, forany given request, the request parser 108 may determine that theknowledge bases 122A and 122B are to be accessed. If there is noidentified correlation between the knowledge bases 122A and 122B, or ifthere is no knowledge dependency, the request parser 108 may determinethat the knowledge bases 122A and 122B can be accessed in an independentfashion. In this case, the work package engine 118 sends a first requestto the knowledge base 122A to acquire knowledge and sends a second,independent request to the knowledge base 122B to acquire knowledge.Because these requests are sent independently of each other, the workpackage engine 118 is capable of acquiring knowledge from the knowledgebases 122A and 122B in an efficient fashion.

In one implementation, knowledge acquisition engine 106 is able toidentify knowledge bases that are to be accessed by analyzing searchrequests sent from the client system 102 and matching search informationfrom these requests with contents of these knowledge bases. For example,the request parser 108 may determine that knowledge bases 122A and 122Bare to be accessed for acquiring knowledge in response to receiving asearch request from the client system 102. If the work package engine118 does not obtain knowledge from the knowledge bases 122A and 122Bthat satisfies the search request, the query script generator 110 iscapable of generating another script having instructions that, whenexecuted by the work package engine 118, cause additional knowledgebases to be accessed. For example, based upon a previously identifiedrelationship between the knowledge base 122A and 122C, the work packageengine 118 may access each of the knowledge bases 122A, 122B, and 122Cin a second iteration. The work package engine 118 then determineswhether the knowledge acquired from the knowledge bases 122A, 122B, and122C satisfies the search request received from the client system 102.If not, the work package engine 118 can continue to execute additionalscripts that are generated by the query script generator 110 thatidentify additional knowledge bases, such as the knowledge base 122D,that have previously identified relationships with the knowledge bases122A, 122B, and/or 122C and that may contain knowledge that is useful tothe client system 102.

In one implementation, the knowledge repository 120 comprises a singledata store and each of the knowledge bases 122A, 122B, 122C, and 122Dcomprise a separate portion of the single data store. In anotherimplementation, the knowledge bases 122A, 122B, 122C, and 122D comprisefour separate data stores for storing knowledge. In this implementation,the knowledge repository 120 comprises a logical collection of thesefour separate data stores.

FIG. 2 is a use-case diagram 200 showing interactions between variouscomponents in FIG. 1, according to one implementation. The actors thatare shown in the example of FIG. 2 are the client system 102, theinteraction component 104, the request parser 108, the natural languageparser 116, the query script generator 110, the work package engine 118,the knowledge repository 120, the navigation index generator 112, andthe visualization output generator 114.

As shown in the use-case diagram 200, the client system 102 first sendsa search request to the interaction component 104. As described above,the search request may have many different forms. For example, thesearch request may include free-form text in search terms or keywords.Alternatively, the search request may include data having apredetermined form or structure. Various data elements may be containedin a data structure that is included within the request. In oneimplementation, a user of the client system 102 enters free-form text ina text-entry field, such as one or more search terms. The interactioncomponent 104 processed the free-form text and creates a data structureto be included within a search request. The data structure includes aset of fields to store data in predefined formats. For example, a firstfield may be used to store product information and a second field may beused to store customer information. In this example, the interactioncomponent 104 is capable of mapping the information contained within thefree-form text into the first and second fields of the data structurethat is contained within the request.

In another implementation, the user of the client system 102 selectsvalues from menus within a graphical user interface (GUI). These menuscontain predefined options for various items that may be relevant to theuser's search. The client system 102 then creates a search request byincluding the selected values. These values may include customer,product, or other forms of information.

The interaction component 104 sends the request to the request parser108 of the knowledge acquisition engine 106. The interaction component104 manages the communication interface with the knowledge acquisitionengine 106. In the implementation shown in FIG. 2, the request includessearch terms or keywords in a specific language, such as English or amixture of multiple languages. The request parser 108 uses the naturallanguage parser 116 to process the request. The natural language parser116 is capable of recognizing characters within the search terms orkeywords and using a set of rules to determine the content (and incertain cases, the context) of the search terms or keywords. Forexample, the natural language parser 116 may be capable of identifyingthe language of each term and interpreting certain characters in theterms or keywords to determine that the request relates to specificproduct or customer information. The request parser 108 is able to usethe output of the natural language parser 116 to determine whichknowledge bases are to be accessed during the search. For example, ifthe search terms or keywords relate to both product and customerinformation, the request parser 108 is able to determine that the searchoperation is to access knowledge bases containing product and customerinformation. These knowledge bases contain knowledge of a differenttype. The request parser 108 is able to determine that more than oneknowledge base is to be accessed based upon the search terms orkeywords. In addition, the request parser 108 may be able to determinethat two or more knowledge bases are to be accessed in a specific order.For example, the customer information may need to be accessed before theproduct information. The customer information may include location oridentification data that is needed as input when accessing the specificproduct information that is needed. In this case, the request parser 108determines that the knowledge base for customer information is to beaccessed before the knowledge base for product information.

The request parser 108 provides information related to the processedsearch request to the query script generator 110. The query scriptgenerator 110 then generates an executable script containingcomputer-executable instructions. When executed, these instructionscause the knowledge acquisition engine 106 to access the identifiedknowledge bases in a predetermined order. As described above, therequest parser 108 is able to identify the knowledge bases (such as theknowledge bases 122A and 122B) within the knowledge repository 120 thatare to be accessed during a search operation. These knowledge bases areidentified from the content and/or the context of the request receivedfrom the client system 102. In this fashion, the client system 102 needonly provide a search request, which may include a series of searchterms or keywords. The client system 102 need not identify the specificknowledge bases that are to be accessed during the search operation. Theknowledge acquisition engine 106 has the intelligence to process therequest sent by the client system 102 and identify these knowledgebases. The instructions contained within the script generated by thescript generator 110 include references to these identified knowledgebases.

As shown in FIG. 2, the script generator 110 provides the generatedscript back to the request parser 108. The request parser 108 thenprovides the script to the work package engine 118. The work packageengine 118 executes the instructions contained within the script andcollects knowledge that is acquired from the various knowledge basescontained within the knowledge repository 120. For example, in onescenario, the work package engine 118 executes the instructions in thegenerated script to acquire knowledge from the knowledge bases 122A and122B in sequential fashion. The work package engine 118 sends a firstrequest to the knowledge bases 122A to obtain customer information. Thisfirst request includes information from the request sent by the clientsystem 102 and processed by the request parser 108. The work packageengine 118 sends a second request to the knowledge bases 122B to obtainproduct information. The second request may include information from therequest sent by the client system 102 but also includes some of theknowledge acquired from the knowledge base 122A relating to customerinformation. For example, the second request may include specificcustomer information that is needed to obtain specified productinformation from the knowledge base 122B. The request parser 108 usesthe original request sent by the client system 102 to determine that theknowledge base 122A is to be accessed before the knowledge base 122B.This determination is then provided to the script generator 110, whichgenerates the script that is executed by the work package engine 118.

In one implementation, the work package engine 118 is capable of sendingrequests to multiple knowledge bases within the knowledge repository 120in parallel. For example, the work package engine 118 is capable ofsending one request to the knowledge base 122A and another request inparallel to the knowledge base 122C, and then later sending a request tothe knowledge base 122B and one in parallel to the knowledge base 122D.The script generated by the script generator 110 includes instructionsto access these knowledge bases in a parallel fashion. For example, ifthe knowledge base 122A includes customer information and the knowledgebase 122C includes store information, the request parser 108 may be ableto determine from the incoming request sent by the client system 102that these knowledge bases may be accessed in parallel. In this case,the work package engine 118 provides only information from the requestreceived by the client system 102 in the distinct requests that are sentin parallel to the knowledge base 122A and to the knowledge base 122C(or to the knowledge base 122B and to the knowledge base 122D) whenacquiring knowledge. In one implementation, requests may be sent inparallel to the same knowledge base during a search operation. Forexample, the work package engine 118 may send two separate requests tothe knowledge base 122A in parallel and then later send additionalrequests to one of the other knowledge bases 122B, 122C, and/or 122D.

Once the work package engine 118 acquires the knowledge from each of theknowledge bases, such as the knowledge bases 122A and 122B, it providesthis knowledge to the navigation index generator 112. The navigationindex generator 112 generates a navigation index for the knowledge thathas been acquired from the knowledge bases. The navigation index allowsa use to navigate through and identify knowledge that has been collectedfrom the knowledge bases 122A, 122B, 122C, and/or 122D. The work packageengine 118 also invokes the visualization output generator 114. Thevisualization output generator 114 generates a visualization scheme thatcan be used by the client system 102 when navigating through theacquired knowledge. For example, the visualization output generator 114may generate a specific form of graphical user interface (GUI) that iswell suited for display purposes on the client system 102.

The work package engine 118 provides the knowledge acquired from theknowledge repository 120, along with the generated navigation index andvisualization output, to the request parser 108. The request parser 108then sends all of this information back to the client system 102 via theinteraction component 104. The client system 102 uses the generatedvisualization output and navigation index to display the acquiredknowledge to a user within a GUI. The user is able to view the acquiredknowledge in an understandable form, and is also able to use thenavigation index to navigate through the information retrieved from theknowledge repository 120.

FIG. 3 is a flow diagram of a method 300 to acquire knowledge from theknowledge repository 120 shown in FIG. 1, according to oneimplementation. In this implementation, the method 300 is performed byone or more of the components of the knowledge acquisition engine 106,such as the request parser 108, the natural language parser 116, thequery script generator 110, and/or the work package engine 118.

If the request sent from the client system 102 includes a set ofpredefined selections 302, then the request parser 108 matches theseselections to knowledge base contents within the knowledge baserepository 120 in an action 310. These predefined selections 302 mayinclude selections of values from search fields that are displayed in agraphical user interface (GUI) to a user on the client system 102. Theseselections can then be matched with contents of knowledge bases in theknowledge repository, such as the knowledge bases 122A or 1228. In oneimplementation, the request parser 108 accesses an index for theknowledge repository 120 to match these selections. This index has beencompiled bases upon the content contained within the knowledge bases122A, 122B, 122C, and/or 122D in the knowledge repository. In oneimplementation, there is one index associated with each knowledge base.

If the request sent from the client system 102 includes free text in afree text request 304, then the request parser 108 and the naturallanguage parser 116 parse the free text into keywords in an action 306.The free text contains one or more textual characters. The parsers 108and 116 are able to process the free text and identify one or morekeywords from the textual characters. In one implementation, the requestparser 108 uses the identified keywords to construct correspondingvalues for predefined fields. These keywords or corresponding values arethen matched against predefined selections in an action 308. The requestparser 108 manages the predefined selections within the knowledgeacquisition engine, 106 and determines which selections match theidentified keywords or corresponding values. These matched selectionsare then checked against the knowledge base contents in the action 310.

In an action 312, a list of knowledge bases from the knowledgerepository 120 is selected. These knowledge bases correspond to thosecontaining the contents matched in the action 310. For example, if thematched contents correspond to specific customer and productinformation, the knowledge bases 122A and 122B may be selected in theaction 312.

At a checkpoint 314, the query script generator 110 determines if thereis a predefined sequence for accessing the selected knowledge bases. Forexample, the query script generator 110 may use a rule specifying that,based upon the request received from the client system 102, theknowledge base 122A is to be accessed before the knowledge base 122B. Inone scenario, the rule may specify that the knowledge base 122A is to beaccessed first regardless of the contents of the request received fromthe client system 102. If there is no predefined sequence, then thequery script generator 110 retrieves a recommendation from learnedfeedbacks in 316. In one implementation, this recommendation comes froma learning engine that is contained within the knowledge acquisitionengine 106. This recommendation for an access sequence is based uponfeedback and knowledge received from prior iterations or previousrequests sent to the knowledge repository 120 and selections made by theuser. For example, if previous knowledge acquisition has occurredsuccessfully when accessing the knowledge base 122A before the knowledgebase 122B, the recommendation may specify that the knowledge base 122Ashould be accessed first. The knowledge acquisition engine 106 accessesthe selection/sequence map 318 when retrieving a recommendation in theaction 316. The map 318 includes sequences that are associated withspecific selections of knowledge made by a user on the client system 102during prior search operations.

If there is a predefined sequence determined at the checkpoint 314, thissequence is processed for later use within the knowledge acquisitionengine 106. In one implementation, the predefined sequence providing asequential access order is contained within a data store that isaccessible by the knowledge acquisition engine 106 when generatingscripts. In another implementation, the predefined sequence may bespecified in the request provided by either the user or a businessapplication. For example, if the knowledge base 122A contains customerinformation and the knowledge base 122B contains product information,the predefined sequence may indicate that the search request that issent to the knowledge base 122B depends upon first receiving certainsearch results from the knowledge base 122A. At a checkpoint 320, theknowledge acquisition engine 106 determines if there is a predefinedcorrelation between the knowledge bases that are to be accessed during asearch operation. For example, if the knowledge bases 122A and 122B areto be accessed during a search operation, a predefined correlation mayspecify that whenever the knowledge base 122A is to be accessed, theknowledge base 122B must also be accessed based upon dependencyconsiderations. For example, the knowledge base 122C may containsolutions for help desks and the knowledge base 122D may contain variousdocuments that may be attached to solutions. There is a contentcorrelation between the knowledge bases 122C and 122D, so when one ofthese knowledge bases 122C or 122D is in the list of knowledge baseselection, the other knowledge base is also included for scriptgeneration, even if it is not specifically selected (according to oneimplementation).

If there is no predefined correlation that is identified at thecheckpoint 320, the knowledge acquisition engine 106 retrieves knowledgebase content correlation in an action 322. In one implementation, thecorrelation is based upon learned feedbacks from prior search operationsand is processed by a learning engine. The knowledge acquisition engine106 retrieves the knowledge base content correlation from a data store324, which stores dependency information for the knowledge bases 122A,122B, 122C, and 122D.

In an action 326, the query script generator 110 creates a set ofacquisition scripts for sequential retrieval of knowledge from theknowledge bases 122A, 122B, 122C, and/or 122D in the knowledgerepository 120. In the action 326, the query script generator 110generates instructions that are stored within the acquisition scripts toaccess the knowledge bases 122A, 122B, 122C, and/or 122D in a sequentialorder according to the sequence and correlation rules determined inprior actions. These scripts are also stored by the query scriptgenerator 110, according to one implementation. In one implementation,the query script generator 110 also generates instructions for thesescripts that provide parallel access of the knowledge bases 122A, 122B,122C, and/or 122D. For example, the sequence and correlation rules mayallow the knowledge bases 122A and 122C or 122B and 122B to be accessedin parallel. In this instance, script instructions generated by thequery script generator 110 may specify that these knowledge bases beaccessed in parallel.

In an action 330, the query script generator 110 completes generation ofthe scripts for execution by the work package engine 118. In the action330, the query script generator 110 generates these scripts according toa format that is recognizable and usable by the work package engine 118.In certain instances, the work package engine 118 may be replaced by adifferent version or type of an engine that provides a differentinterface. The query script generator 110 is capable of generatingscripts that conform to the format and interface specified by the enginethat is used within the knowledge acquisition engine 106, such as thework package engine 118.

In an action 336, the knowledge acquisition engine 106 activates thework package engine 118 to acquire knowledge for all of the acquisitionscripts. In the action 336, the work package engine 118 executes theinstructions in these scripts to acquire knowledge from the knowledgebases 122A, 122B, 122C, and/or 122D. In one implementation, the workpackage engine 118 executes instructions from a master script to accessone or more of the knowledge bases 122A, 122B, 122C, and/or 122D. In oneimplementation, the work package engine 118 executes instructions fromone script for each knowledge base that is accessed. For example, thework package engine 118 may execute instructions from a first script toacquire knowledge from the knowledge base 122A and may then executeinstructions from a second script to acquire knowledge from theknowledge base 122B. The query script generator 110 generates theseseparate scripts and specifies the order in which these scripts are tobe executed by the work package engine 118. For example, the workpackage engine 118 may execute first and second scripts in serialfashion to acquire knowledge from the knowledge bases 122A and 122B.Subsequently, the work package engine 118 may execute third and fourthscripts in parallel to acquire knowledge from the knowledge bases 122Cand 122D.

At a checkpoint 338, the work package engine 118 collects the matchedknowledge that has been identified and acquired from the knowledge bases122A, 122B, 122C, and/or 122D based on a set of generated scripts. Ifthe matched result has low hitting scores from the acquired knowledgebased upon the original request or no matched results at all, an action332 is triggered to generate one or more new scripts. The way in whichthe new scripts are generated may depend on a set of predefined rules.For example, a rule may relax the search restriction by removing theforced sequence between two knowledge bases. Another rule may change the“And” operation in a search term query to an “Or” operation. After theadditional rules are implemented, the new scripts can be generated inaction 330. However, such re-generation steps can be discontinued ifsome knowledge is located or a maximal number of trails is reached,according to one implementation. In this scenario, the result(s)corresponding to the knowledge that has been identified and acquired canbe returned back to the user.

In an action 340, there is a final selection by the user if the acquiredknowledge does match the user criteria. This selection occurs on theclient system 102 after the acquired knowledge has been sent to theclient system 102 by the knowledge acquisition engine 106. All of theknowledge acquired from the knowledge bases 122A, 122B, 122C, and/or122D is compiled by the work package engine 118 and processed by thenavigation index generator 112 and the visualization output generator114 before it is sent to the client system 102. The acquired knowledgeis then displayed to the user on a graphical user interface (GUI). Forexample, the acquired knowledge may include two packages of knowledgeacquired during execution of two separate scripts by the work packageengine 118. Within the GUI, two entries may be displayed to the user,wherein each entry corresponds to one of the packages of knowledge. Theuser may then select one of these two entries within the GUI.

Once the user has made a final selection in an action 340, the knowledgeacquisition engine 106 updates the selection/sequence map 318 in anaction 334 according to the final selection. The map 318 is updated toreflect the specific selection made by the user and the sequence thatwas used to acquire information from one or more of the knowledge bases122A, 122B, 122C, and/or 122D. The knowledge acquisition engine 106determines the specific access sequence that was used to acquire theknowledge corresponding to the user's selection. For example, if theuser selects an entry corresponding to a particular knowledge entity orpackage of knowledge, the knowledge acquisition engine 106 may determinethat the knowledge base 122A was accessed prior to the knowledge base122B to acquire this knowledge. Then, the usage of sequence of theknowledge base 122A to 122B is updated to the corresponding selection.When there are many knowledge bases involved in the selection, it maynot be practical to update all possible combinations of the sequencesand selections, so only the selections and sequences that are commonlyused are recorded in the map 318, according to one implementation.

In an action 328, the knowledge acquisition engine 106 also updatesknowledge base dependency information in the knowledge base contentcorrelation data store 324. The knowledge base dependency can be derivedfrom the script used to acquire the knowledge. For each knowledge entityidentified by user's action 340, there is a corresponding scriptgenerated in the action 330 that leads to the entity. Therefore, theacquisition steps to access other knowledge bases are used whenidentifying the specific knowledge entity. For example, a specificknowledge entity may be located in the knowledge base 122C and may beidentified by a script that includes instructions to access theknowledge bases in the following order: 122A, 122B, 122C, 122D. Sincethe knowledge entity is located in the knowledge base 122C, the usefulinformation of knowledge dependency can be represented as a set of pairsincluding (122A, 122B), (122B, 122C), and (122A, 122C). Then the contentcorrelations can be updated for the three pairs of knowledge bases. Theknowledge base 122D is not included within this set of pairs because itis not needed when accessing the knowledge entity using the accesssequence shown above. However, each of the knowledge bases 122A and 122Bmay contain information that is needed to access the knowledge entitycontained in the knowledge base 122C. For example, certain knowledgethat is acquired from the knowledge base 122A may be used as input toacquire additional knowledge from the knowledge base 122B. Thisadditional knowledge may then be used as input to access the knowledgeentity contained within the knowledge base 122C. In this case, thecorrelation between (122A, 122B) and (122B, 122C) is stronger than(122A, 122C). In general, however, the weighting of such correlations isbased upon the dependencies between the various knowledge bases thatdetermines how a specific knowledge entity is accessed from a givenknowledge base.

FIG. 4 is a block diagram of a computing device 400 that may be part ofthe client system 102 or the knowledge acquisition engine 106 shown inFIG. 1. The computing system 400 includes a processor 402, a memory 404,a storage device 406, a network adaptor 408, and an input/output device410. The components 402, 404, 406, 408, and 410 are interconnected usinga system bus. The processor 402 is capable of processing instructionsfor execution within the computing system 400. In one implementation,the processor 402 is a single-threaded processor. In anotherimplementation, the processor 402 is a multi-threaded processor. Theprocessor 402 is capable of processing instructions stored in the memory404.

The memory 404 stores information within the computing system 400. Inone implementation, the memory 404 is a computer-readable medium. Incertain implementations, the memory 404 is either a volatile or anon-volatile memory unit.

The storage device 406 is capable of providing mass storage for thecomputing system 400. For example, the storage device 406 may providedatabase storage for the computing system 400. In one implementation,the storage device 406 is a computer-readable medium. In variousdifferent implementations, the storage device 406 may be a floppy diskdevice, a hard disk device, an optical disk device, or a tape device.

The network adaptor 408 provides an interface to external networkdevices. For example, the network adaptor 408 may be contained withinboth the client system 102 and the knowledge acquisition engine 106 toprovide network interface connectivity between these two entities. Inone implementation, the network adaptor 408 is a wireless adaptor thatprovides wireless connectivity.

In one implementation, a computer program product is tangibly embodiedin an information carrier. The computer program product containsinstructions that, when executed, perform one or more methods, such asthose described above. The information carrier is a computer- ormachine-readable medium, such as the memory 404, the storage device 406,or a propagated signal.

The input/output device 410 provides input/output operations for thecomputing system 400. The input/output device 410 may include, forexample, a keyboard, a pointing device, and/or a display device.

A number of implementations have been described above. Nevertheless, itwill be understood that various modifications may be made withoutdeparting from the spirit and scope of these implementations.Accordingly, other implementations are within the scope of the followingclaims.

What is claimed is:
 1. A method comprising: acquiring knowledge fromfirst and second knowledge bases in a knowledge repository; identifyingthe first and second knowledge bases within the knowledge repository byanalyzing a search request received from a client system, the firstknowledge base containing knowledge of a first type, and the secondknowledge base containing knowledge of a second type, wherein theanalyzing comprises parsing the search request comprising textualcharacters to identify one or more key words, wherein the one or morekey words are used to construct values for predefined fields; and uponidentification of the first and second knowledge bases, generatinginstructions that, when executed, cause first and second requests to besent to the knowledge repository in sequential fashion to acquireknowledge from the first and second knowledge bases, such that thesecond request is sent after the first request, and such that the secondrequest includes knowledge of the first type from the first knowledgebase acquired in response to the first request.
 2. The method of claim1, wherein the search request contains textual characters provided by auser.
 3. The method of claim 1, wherein the identifying the first andsecond knowledge bases includes matching content of the search requestwith content of the first and second knowledge bases.
 4. The method ofclaim 3, wherein matching content of the search request with content ofthe first and second knowledge bases includes accessing an index thathas been compiled from the first or second knowledge base.
 5. The methodof claim 4, wherein the index has been compiled from both the first andsecond knowledge bases.
 6. The method of claim 1, further comprising: ifno knowledge of the second type is acquired from the second knowledgebase that satisfies the search request received from the client system,modifying the instructions that, when executed, cause a third request tobe sent to the first knowledge base to acquire knowledge that satisfiesthe search request.
 7. The method of claim 1, further comprising: if noknowledge of the second type is acquired from the second knowledge basethat satisfies the search request received from the client system,modifying the instructions that, when executed, cause a third request tobe sent to the second knowledge base to acquire knowledge that satisfiesthe search request.
 8. The method of claim 1, wherein the search requestcontains values provided by a user for predefined fields.
 9. The methodof claim 1, wherein the instructions, when executed, cause a data storeto be accessed, the data store containing information that specifies asequential order in which to send the first and second requests.
 10. Themethod of claim 9, wherein the information specifying the sequentialorder is based upon knowledge acquired in response to previous requestssent to the knowledge repository.
 11. The method of claim 1, wherein theinstructions, when executed, cause third and fourth requests to be sentto the knowledge repository in sequential fashion to acquire knowledgefrom the first and second knowledge bases, such that the fourth requestis sent after the third request, such that the fourth request includesknowledge of the first type from the first knowledge base acquired inresponse to the third request, and such that the third request is sentin parallel with the first request.
 12. The method of claim 1, furthercomprising: receiving the search request from the client system;executing the instructions; and sending the knowledge acquired from thefirst and second knowledge bases to the client system.
 13. The method ofclaim 12, further comprising: generating a navigation index for theknowledge acquired from the first and second knowledge bases; anddisplaying the navigation index to allow a user on the client system toidentify the knowledge acquired from the first and second knowledgebases.
 14. The method of claim 1, wherein the first knowledge basecomprises a first separate data store, and wherein the second knowledgebase comprises a second separate data store.
 15. The method of claim 1,wherein the client system includes a mobile device.
 16. Acomputer-readable storage medium including instructions that, whenexecuted, perform operations comprising: acquiring knowledge from firstand second knowledge bases in a knowledge repository; identifying thefirst and second knowledge bases within the knowledge repository byanalyzing a search request received from a client system, the firstknowledge base containing knowledge of a first type, and the secondknowledge base containing knowledge of a second type, wherein theanalyzing comprises parsing the search request comprising textualcharacters to identify one or more key words, wherein the one or morekey words are used to construct values for predefined fields; and uponidentification of the first and second knowledge bases, generatinginstructions that, when executed, cause first and second requests to besent to the knowledge repository in sequential fashion to acquireknowledge from the first and second knowledge bases, such that thesecond request is sent after the first request, and such that the secondrequest includes knowledge of the first type from the first knowledgebase acquired in response to the first request.